Machine Learning
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Logistic Regression, AdaBoost and Bregman Distances
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Training a Support Vector Machine in the Primal
Neural Computation
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
The Journal of Machine Learning Research
Solving multiclass support vector machines with LaRank
Proceedings of the 24th international conference on Machine learning
Trust region Newton methods for large-scale logistic regression
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
A scalable modular convex solver for regularized risk minimization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimized cutting plane algorithm for support vector machines
Proceedings of the 25th international conference on Machine learning
Stopping conditions for exact computation of leave-one-out error in support vector machines
Proceedings of the 25th international conference on Machine learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Cutting-plane training of structural SVMs
Machine Learning
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning
The Journal of Machine Learning Research
The SHOGUN Machine Learning Toolbox
The Journal of Machine Learning Research
Training linear ranking SVMs in linearithmic time using red-black trees
Pattern Recognition Letters
Mal-ID: automatic malware detection using common segment analysis and meta-features
The Journal of Machine Learning Research
Estimating building simulation parameters via Bayesian structure learning
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Hi-index | 0.00 |
We have developed an optimized cutting plane algorithm (OCA) for solving large-scale risk minimization problems. We prove that the number of iterations OCA requires to converge to a ε precise solution is approximately linear in the sample size. We also derive OCAS, an OCA-based linear binary Support Vector Machine (SVM) solver, and OCAM, a linear multi-class SVM solver. In an extensive empirical evaluation we show that OCAS outperforms current state-of-the-art SVM solvers like SVMlight, SVMperf and BMRM, achieving speedup factor more than 1,200 over SVMlight on some data sets and speedup factor of 29 over SVMperf, while obtaining the same precise support vector solution. OCAS, even in the early optimization steps, often shows faster convergence than the currently prevailing approximative methods in this domain, SGD and Pegasos. In addition, our proposed linear multi-class SVM solver, OCAM, achieves speedups of factor of up to 10 compared to SVMmulti-class. Finally, we use OCAS and OCAM in two real-world applications, the problem of human acceptor splice site detection and malware detection. Effectively parallelizing OCAS, we achieve state-of-the-art results on an acceptor splice site recognition problem only by being able to learn from all the available 50 million examples in a 12-million-dimensional feature space. Source code, data sets and scripts to reproduce the experiments are available at http://cmp.felk.cvut.cz/~xfrancv/ocas/html/.